Research
What we decided to trust
Qualitative findings from thirteen interviews with founders, product leaders, and engineers on how they decide what to believe in their product data.
The question we asked
How do you decide what to trust in your product data — and when does a bad number become a decision that costs you? We put that question to thirteen people who build for a living: a consumer-app CEO, a solo AI founder, a fifteen-year product veteran, an engineering manager running an army of agents, a CTO rebuilding after his first product died, and a healthcare product officer who is not allowed to watch his users at all.
What is inside
- —The headline finding, and where every participant drew the line between a number and a person.
- —Ten themes, each carried by the interview quotes it came from.
- —Five frameworks: seven classes of product data, the access axis, where the two cross, three registers of “data-driven” with two tests, and four phases of a decision system.
- —The method, what is deliberately left out, and the colophon.
How it was made
Thirteen semi-structured interviews, conducted and edited by A Founder's Friend, each reviewed and signed off by the participant — named or anonymous, their choice. This research was sponsored by Skene.ai. Editorial independence was preserved throughout: Skene did not direct the questions, the analysis, or the conclusions.
Final edit · 13 September 2026
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